SNP & TAD
Renee Matthews
2025-06-10
Last updated: 2025-07-29
Checks: 7 0
Knit directory: ATAC_learning/
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| File | Version | Author | Date | Message |
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| Rmd | f8c0205 | reneeisnowhere | 2025-07-29 | adding dars loci |
| html | b1638d9 | reneeisnowhere | 2025-07-29 | Build site. |
| Rmd | ad110ea | reneeisnowhere | 2025-07-29 | updates |
| html | 1429820 | reneeisnowhere | 2025-07-21 | Build site. |
| Rmd | b838697 | reneeisnowhere | 2025-07-21 | adding in SNP overlap heatmap |
| html | 1a9df02 | reneeisnowhere | 2025-07-09 | Build site. |
| Rmd | 0fa75c3 | reneeisnowhere | 2025-07-09 | wflow_publish("analysis/SNP_TAD_peaks.Rmd") |
library(tidyverse)
library(kableExtra)
library(broom)
library(RColorBrewer)
library(ChIPseeker)
library(ChIPpeakAnno)
library("TxDb.Hsapiens.UCSC.hg38.knownGene")
library("org.Hs.eg.db")
library(rtracklayer)
library(edgeR)
library(ggfortify)
library(limma)
library(readr)
library(BiocGenerics)
library(gridExtra)
library(VennDiagram)
library(scales)
library(BiocParallel)
library(ggpubr)
library(devtools)
library(biomaRt)
library(eulerr)
library(smplot2)
library(genomation)
library(ggsignif)
library(plyranges)
library(ggrepel)
library(epitools)
library(circlize)
library(readxl)
library(ComplexHeatmap)
library(gwascat)
library(liftOver)Loading data frames
### Pulling the all regions granges list from the motif list of lists
Motif_list_gr <- readRDS("data/Final_four_data/re_analysis/Motif_list_granges.RDS")
### no change motif_list_gr names so they do not overwrite the dataframes
names(Motif_list_gr) <- paste0(names(Motif_list_gr), "_gr")
list2env(Motif_list_gr[10],envir= .GlobalEnv)<environment: R_GlobalEnv>annotated_DARs<- readRDS("data/Final_four_data/re_analysis/DOX_DAR_annotated_peaks_chipannno.RDS")
Left_ventricle_TAD <- import(con = "C://Users/renee/Downloads/hg38.TADs/hg38/VentricleLeft_STL003_Leung_2015-raw_TADs.txt", format = "bed",genome="hg38")
mcols(Left_ventricle_TAD)$TAD_id <- paste0("TAD_", seq_along(Left_ventricle_TAD))
# mcols(Left_ventricle_TAD)$name <- Left_ventricle_TAD$TAD_id
##exporting the LEFt ventricle tad info for IGV visualization
# export(Left_ventricle_TAD, con = "data/Final_four_data/re_analysis/Other_bed_files/TAD_regions.bed", format="bed")
Schneider_all_SNPS <- read_delim("data/other_papers/Schneider_all_SNPS.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE)
Schneider_all_SNPS_df <- Schneider_all_SNPS %>%
dplyr::rename("RSID"="#Uploaded_variation") %>%
dplyr::select(RSID,Location,SYMBOL,Gene, SOURCE) %>%
distinct(RSID,Location,SYMBOL,.keep_all = TRUE) %>%
dplyr::rename("Close_SYMBOL"="SYMBOL") %>%
dplyr::filter(!str_starts(Location, "H")) %>%
separate_wider_delim(Location,delim=":",names=c("Chr","Coords")) %>%
separate_wider_delim(Coords,delim= "-", names= c("Start","End")) %>%
mutate(Chr=paste0("chr",Chr)) %>%
group_by(RSID) %>%
reframe(Chr=unique(Chr),
Start=unique(Start),
End=unique(End),
Close_SYMBOL=paste(unique(Close_SYMBOL),collapse=";"),
Gene=paste(Gene,collapse=";"),
SOURCE=paste(SOURCE,collapse=";")
) %>%
GRanges() %>% as.data.frame
schneider_gr <-Schneider_all_SNPS_df%>%
dplyr::select(seqnames,start,end,RSID:SOURCE) %>%
distinct() %>%
GRanges()
# export(schneider_gr, con = "data/Final_four_data/re_analysis/Other_bed_files/CardiotoxSNPs.bed", format="bed")
toptable_results <- readRDS("data/Final_four_data/re_analysis/Toptable_results.RDS")
all_results <- toptable_results %>%
imap(~ .x %>% tibble::rownames_to_column(var = "rowname") %>%
mutate(source = .y)) %>%
bind_rows()
all_results_pivot <- all_results %>%
dplyr::select(genes,logFC,source) %>%
pivot_wider(., id_cols = genes, names_from = source, values_from = logFC) %>%
dplyr::select(genes,DOX_3,EPI_3,DNR_3,MTX_3,TRZ_3,DOX_24,EPI_24,DNR_24,MTX_24,TRZ_24)
toplistall_RNA <- readRDS("data/other_papers/toplistall_RNA.RDS") %>%
mutate(logFC = logFC*(-1))
Assigned_genes_toPeak <- annotated_DARs$DOX_24 %>% as.data.frame() %>%
dplyr::select(mcols.genes,annotation, geneId, distanceToTSS) %>%
dplyr::rename("Peakid"=mcols.genes)
RNA_results <-
toplistall_RNA %>%
dplyr::select(time:logFC) %>%
tidyr::unite("sample",time, id) %>%
pivot_wider(., id_cols = c(ENTREZID,SYMBOL),names_from = sample, values_from = logFC) %>%
rename_with(~ str_replace(., "hours", "RNA"))
Peak_gene_RNA_LFC <- Assigned_genes_toPeak %>%
left_join(., RNA_results, by =c("geneId"="ENTREZID"))
entrez_ids <- Assigned_genes_toPeak$geneId
gene_info <- AnnotationDbi::select(
org.Hs.eg.db,
keys = entrez_ids,
columns = c("SYMBOL"),
keytype = "ENTREZID"
)
gene_info_collapsed <- gene_info %>%
group_by(ENTREZID) %>%
summarise(SYMBOL = paste(unique(SYMBOL), collapse = ","), .groups = "drop")
DOX_DAR_24hr_table <- annotated_DARs$DOX_24 %>%
as.data.frame()
Top2b_peaks <- import(con="data/other_papers/ChIP3_TOP2B_CM_87-1.bed",format = "bed",genome="hg38")
# for_export <- DOX_24_DAR%>%
# # as.data.frame() %>%
# ### mark significance with color
# mutate(sig_24=if_else(mcols.adj.P.Val<0.05,"TRUE","FALSE")) %>%
# dplyr::select(seqnames:mcols.genes,sig_24) %>%
# GRanges
#
# ### add to the granges thingy for exporting
# mcols(for_export)$itemRgb <- ifelse(mcols(for_export)$sig_24,
# "255,0,0", # red for significant
# "190,190,190") # gray for not significant
# mcols(for_export)$sig_24 <- as.logical(mcols(for_export)$sig_24)
# mcols(for_export)$itemRgb <- as.character(mcols(for_export)$itemRgb)
#
# bed_df <- data.frame(
# seqnames = seqnames(for_export),
# start = start(for_export) - 1, # BED is 0-based
# end = end(for_export),
# strand = "*",
# thickStart = start(for_export) - 1,
# thickEnd = end(for_export),
# itemRgb = ifelse(mcols(for_export)$sig_24, "255,0,0", "190,190,190"),
# stringsAsFactors = FALSE)
# # )
#
# write.table(bed_df,
# file = "data/Final_four_data/re_analysis/Other_bed_files/DOX_24hour_sig_notsig_regions.bed",
# quote = FALSE, sep = "\t", row.names = FALSE, col.names = FALSE)Enrichment test of sig DAR and non-sig DAR of DOX within SNP-containing TADS
test_ol <- join_overlap_intersect(Left_ventricle_TAD, schneider_gr)
df <- as.data.frame(test_ol, row.names = NULL)
TAD_SNP_ol <- test_ol %>% as.data.frame() %>%
distinct(TAD_id, RSID)
peak_ol <- join_overlap_intersect(all_regions_gr, Left_ventricle_TAD)
TAD_SNP_Peak_ol <- peak_ol %>%
as.data.frame() %>%
dplyr::filter(TAD_id %in% TAD_SNP_ol$TAD_id)
snp_ol <- join_overlap_inner(schneider_gr, Left_ventricle_TAD)
TAD_peak_ol <- peak_ol %>%
as.data.frame() %>%
distinct(Peakid,.keep_all = TRUE)
left_ventricle_ol <- join_overlap_inner(all_regions_gr ,Left_ventricle_TAD) %>%
as.data.frame() %>%
distinct(Peakid,.keep_all = TRUE) %>%
dplyr::filter(TAD_id %in% TAD_SNP_ol$TAD_id)
peak_df <- as.data.frame(left_ventricle_ol)
SNP_df <- as.data.frame(snp_ol)
peak_snp_pairs <- inner_join(peak_df, SNP_df, by = "TAD_id", suffix = c(".peak", ".snp")) %>%
mutate(
peak_center = (start.peak + end.peak) / 2,
distance = abs(peak_center - start.snp) # or any metric you prefer
)reds <- colorRampPalette(brewer.pal(9, "Reds")[3:9])(12)
greens <- colorRampPalette(brewer.pal(9, "Greens")[3:9])(12)
blues <- colorRampPalette(brewer.pal(9, "Blues")[3:9])(12)
purples <- colorRampPalette(brewer.pal(9, "Purples")[3:9])(12)
oranges <- colorRampPalette(brewer.pal(9, "Oranges")[3:9])(12)
tads <- unique(peak_snp_pairs$TAD_id)
num_tads <- length(tads)
color_spectrum <- c(reds, greens, blues, purples, oranges)[1:num_tads]
if (num_tads > length(color_spectrum)) {
stop("Not enough colors for TADs. Add more palettes.")
}
tad_colors <- color_spectrum[1:num_tads]
names(tad_colors) <- tads # Assign color names to TAD IDs
#ha <- HeatmapAnnotation(TAD = df$TAD_id, col = list(TAD = tad_colors))Top2b_overlap_regions <-join_overlap_inner(all_regions_gr ,Top2b_peaks) %>%
as.data.frame() %>%
distinct(Peakid,.keep_all = TRUE) DOX_24_DAR <- as.data.frame(annotated_DARs$DOX_24)
EPI_24_DAR <- as.data.frame(annotated_DARs$EPI_24)
DNR_24_DAR <- as.data.frame(annotated_DARs$DNR_24)
MTX_24_DAR <- as.data.frame(annotated_DARs$MTX_24)
DOX_3_DAR <- as.data.frame(annotated_DARs$DOX_3)
EPI_3_DAR <- as.data.frame(annotated_DARs$EPI_3)
DNR_3_DAR <- as.data.frame(annotated_DARs$DNR_3)
MTX_3_DAR <- as.data.frame(annotated_DARs$MTX_3)
TAD_count_df <- DOX_24_DAR %>%
dplyr::select(mcols.genes, mcols.adj.P.Val,annotation:distanceToTSS) %>%
mutate(sig_24=if_else(mcols.adj.P.Val<0.05,"sig","not_sig")) %>%
mutate(sig_24=factor(sig_24, levels = c("sig","not_sig"))) %>%
mutate(TAD_all_status=if_else(mcols.genes %in% peak_ol$Peakid,"TAD_peak","not_TAD_peak")) %>%
mutate(SNP_TAD_status= if_else(mcols.genes %in% TAD_SNP_Peak_ol$Peakid,"SNP_TAD","not_SNP_TAD")) %>%
mutate(Top2b_peak= if_else(mcols.genes %in% Top2b_overlap_regions$Peakid, "TOP2B_peak","not_TOP2B_peak"))
TAD_count_df %>% #dplyr::filter(TAD_all_status=="TAD_peak") %>%
group_by(sig_24,SNP_TAD_status,TAD_all_status) %>%
tally #%>% # A tibble: 6 × 4
# Groups: sig_24, SNP_TAD_status [4]
sig_24 SNP_TAD_status TAD_all_status n
<fct> <chr> <chr> <int>
1 sig SNP_TAD TAD_peak 2047
2 sig not_SNP_TAD TAD_peak 56865
3 sig not_SNP_TAD not_TAD_peak 5908
4 not_sig SNP_TAD TAD_peak 3111
5 not_sig not_SNP_TAD TAD_peak 78627
6 not_sig not_SNP_TAD not_TAD_peak 8999 # pivot_wider(., id_cols = sig_24, names_from = SNP_TAD_status, values_from = n)
# print("Odds ratio of SNP_TADs across DOXall regions, regardless of TAD_status")
# TAD_count_df %>% #dplyr::filter(TAD_all_status=="TAD_peak") %>%
# group_by(sig_24,SNP_TAD_status) %>%
# tally %>%
# pivot_wider(., id_cols = sig_24, names_from = SNP_TAD_status, values_from = n) %>%
# column_to_rownames( "sig_24") %>% as.matrix() %>%
# # chisq.test()
# epitools::oddsratio()
print("Odds ratio testing proportion SNP-containing TADs of sig-DOX DARs vs non-sig DARs at 24 hours")[1] "Odds ratio testing proportion SNP-containing TADs of sig-DOX DARs vs non-sig DARs at 24 hours"TAD_count_df %>% dplyr::filter(TAD_all_status=="TAD_peak") %>%
group_by(sig_24,SNP_TAD_status) %>%
tally %>%
pivot_wider(., id_cols = sig_24, names_from = SNP_TAD_status, values_from = n) %>%
column_to_rownames( "sig_24") %>% as.matrix() %>%
# chisq.test()
epitools::oddsratio(method = "wald")$data
SNP_TAD not_SNP_TAD Total
sig 2047 56865 58912
not_sig 3111 78627 81738
Total 5158 135492 140650
$measure
NA
odds ratio with 95% C.I. estimate lower upper
sig 1.000000 NA NA
not_sig 0.909797 0.8595486 0.9629828
$p.value
NA
two-sided midp.exact fisher.exact chi.square
sig NA NA NA
not_sig 0.00107761 0.001098901 0.001105101
$correction
[1] FALSE
attr(,"method")
[1] "Unconditional MLE & normal approximation (Wald) CI"TAD_count_df %>%
dplyr::filter(TAD_all_status=="TAD_peak") %>%
group_by(sig_24,SNP_TAD_status) %>%
tally ()%>%
mutate(sig_24=factor(sig_24, levels = c("sig","not_sig"))) %>%
ggplot(.,aes(x=sig_24, y= n,fill=SNP_TAD_status))+
geom_col(position="fill")+
theme_bw()+
ggtitle("Proportion of significant regions by 24 hours")+
ylab("proportion")
# TAD_count_df %>%
# dplyr::filter(TAD_all_status=="TAD_peak") %>%
#
# group_by(sig_24,SNP_TAD_status) %>%
# tally ()%>%
# mutate(sig_24=factor(sig_24, levels = c("sig","not_sig"))) %>%
# ggplot(.,aes(x=SNP_TAD_status, y= n,fill=sig_24))+
# geom_col(position="fill")+
# theme_bw()+
# ggtitle("Proportion of significant regions by 24 hours")+
# ylab("proportion")Proportion of DARs that overlap TOP2B peaks in a TAD
TAD_count_df %>%
dplyr::filter((TAD_all_status=="TAD_peak")) %>%
dplyr::filter(SNP_TAD_status=="SNP_TAD") %>%
group_by(SNP_TAD_status, Top2b_peak, sig_24) %>%
tally() %>%
pivot_wider(., id_cols=sig_24, names_from = Top2b_peak, values_from = n) %>%
print() %>%
column_to_rownames("sig_24") %>%
fisher.test()# A tibble: 2 × 3
sig_24 TOP2B_peak not_TOP2B_peak
<fct> <int> <int>
1 sig 32 2015
2 not_sig 121 2990
Fisher's Exact Test for Count Data
data: .
p-value = 8.347e-07
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.2560764 0.5863054
sample estimates:
odds ratio
0.3924942 Calculating Distance to TAD-SNP from peak
*#### DOX 24 hours
DOX_DAR_sig <- DOX_24_DAR %>%
dplyr::filter(mcols.adj.P.Val<0.05) %>%
distinct (mcols.genes) %>%
dplyr::rename("Peakid"="mcols.genes")
DOX_DAR_sig_3 <- DOX_3_DAR %>%
dplyr::filter(mcols.adj.P.Val<0.05) %>%
distinct (mcols.genes) %>%
dplyr::rename("Peakid"="mcols.genes")
EPI_DAR_sig <- EPI_24_DAR %>%
dplyr::filter(mcols.adj.P.Val<0.05) %>%
distinct (mcols.genes) %>%
dplyr::rename("Peakid"="mcols.genes")
EPI_DAR_sig_3 <- EPI_3_DAR %>%
dplyr::filter(mcols.adj.P.Val<0.05) %>%
distinct (mcols.genes) %>%
dplyr::rename("Peakid"="mcols.genes")
DNR_DAR_sig <- DNR_24_DAR %>%
dplyr::filter(mcols.adj.P.Val<0.05) %>%
distinct (mcols.genes) %>%
dplyr::rename("Peakid"="mcols.genes")
DNR_DAR_sig_3 <- DNR_3_DAR %>%
dplyr::filter(mcols.adj.P.Val<0.05) %>%
distinct (mcols.genes) %>%
dplyr::rename("Peakid"="mcols.genes")
MTX_DAR_sig <- MTX_24_DAR %>%
dplyr::filter(mcols.adj.P.Val<0.05) %>%
distinct (mcols.genes) %>%
dplyr::rename("Peakid"="mcols.genes")
MTX_DAR_sig_3 <- MTX_3_DAR %>%
dplyr::filter(mcols.adj.P.Val<0.05) %>%
distinct (mcols.genes) %>%
dplyr::rename("Peakid"="mcols.genes")
MCF7_DAR_snp_pairs_dist <- readRDS("data/Final_four_data/re_analysis/MCF7_DAR_snp_pairs_dist.RDS") %>%
dplyr::rename("Peakid"=names) %>%
mutate(sig_24="MCF7_DAR")
snp_tad_df <-
join_overlap_inner(schneider_gr, Left_ventricle_TAD) %>%
as_tibble() %>%
dplyr::select(RSID, snp_start = start, snp_chr = seqnames, TAD_id)
peak_tad_df <-
join_overlap_inner(all_regions_gr, Left_ventricle_TAD) %>%
as_tibble() %>%
dplyr::select(Peakid, peak_start = start, peak_chr = seqnames, TAD_id)
peak_snp_pairs <- peak_tad_df %>%
inner_join(snp_tad_df, by = "TAD_id")
peak_snp_pairs_dist <- peak_snp_pairs %>%
mutate(distance = abs(peak_start - snp_start)) %>%
mutate(sig_24= if_else(Peakid %in% DOX_DAR_sig$Peakid, "sig","not_sig"))
peak_snp_pairs_dist %>%
mutate(sig_24=factor(sig_24, levels= c("sig","not_sig"))) %>%
ggplot(., aes(x= sig_24, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig")),
map_signif_level = FALSE, test = "wilcox.test")+
ggtitle("DOX 24 hour distances of DAR-SNP pairs and non-DAR-SNP pairs")
wilcox.test(distance ~ sig_24, data = peak_snp_pairs_dist)
Wilcoxon rank sum test with continuity correction
data: distance by sig_24
W = 9463083, p-value = 0.002185
alternative hypothesis: true location shift is not equal to 0Cardiotox_gwas_df <- peak_snp_pairs_dist %>%
dplyr::filter(sig_24=="sig") %>%
group_by(TAD_id,RSID) %>%
slice_min(order_by = distance, with_ties = FALSE) %>%
ungroup() %>%
arrange(snp_chr,snp_start) %>%
left_join(., all_results_pivot, by=c("Peakid"="genes")) %>%
tidyr::unite(., name,Peakid,RSID)
Cardiotox_gwas_collaped_df <-
peak_snp_pairs_dist %>%
dplyr::filter(sig_24=="sig") %>%
group_by(TAD_id,RSID) %>%
slice_min(order_by = distance, with_ties = FALSE) %>%
ungroup() %>%
arrange(snp_chr,snp_start) %>%
group_by(Peakid, peak_chr, peak_start, TAD_id, sig_24) %>%
summarise(
min_distance = min(distance),
mean_distance = mean(distance),
snp_list = paste(unique(RSID), collapse = ","),
.groups = "drop"
) %>%
left_join(., all_results_pivot, by=c("Peakid"="genes")) %>%
left_join(., Peak_gene_RNA_LFC, by=c("Peakid"="Peakid")) %>%
left_join(.,gene_info_collapsed, by=c("geneId"="ENTREZID")) %>%
mutate(SYMBOL=if_else(is.na(SYMBOL.x),SYMBOL.y,if_else(SYMBOL.x==SYMBOL.y, SYMBOL.x,paste0(SYMBOL.x,"_",SYMBOL.y)))) %>%
tidyr::unite(., name,Peakid,SYMBOL,snp_list) %>%
mutate(snp_dist=case_when(min_distance <2000 ~"2kb",
min_distance > 2000 & min_distance<20000 ~ "20kb",
min_distance >20000 ~">20kb"))peak_snp_pairs_dist_DOX_3 <- peak_snp_pairs %>%
mutate(distance = abs(peak_start - snp_start)) %>%
mutate(sig_3= if_else(Peakid %in% DOX_DAR_sig_3$Peakid, "sig","not_sig"))
peak_snp_pairs_dist_DOX_3 %>%
mutate(sig_3=factor(sig_3, levels= c("sig","not_sig"))) %>%
ggplot(., aes(x= sig_3, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig")),
map_signif_level = FALSE, test = "wilcox.test")
| Version | Author | Date |
|---|---|---|
| 1429820 | reneeisnowhere | 2025-07-21 |
wilcox.test(distance ~ sig_3, data = peak_snp_pairs_dist_DOX_3)
Wilcoxon rank sum test with continuity correction
data: distance by sig_3
W = 837367, p-value = 0.0241
alternative hypothesis: true location shift is not equal to 0Cardiotox_gwas_EPI <- peak_snp_pairs_dist_DOX_3 %>%
dplyr::filter(sig_3=="sig") %>%
group_by(TAD_id,RSID) %>%
slice_min(order_by = distance, with_ties = FALSE) %>%
ungroup() %>%
arrange(snp_chr,snp_start) %>%
left_join(., all_results_pivot, by=c("Peakid"="genes")) %>%
tidyr::unite(., name,Peakid,RSID)Looking at SNPs that directly overlap DARs
snp_peak_ol <- join_overlap_inner(all_regions_gr,schneider_gr)
SNP_DAR_overlap_direct <- snp_peak_ol %>%
as.data.frame() %>%
mutate(Dox_24=if_else(Peakid %in% DOX_DAR_sig$Peakid,"yes","no")) %>%
mutate(Epi_24=if_else(Peakid %in% EPI_DAR_sig$Peakid,"yes","no")) %>%
mutate(Dnr_24=if_else(Peakid %in% DNR_DAR_sig$Peakid,"yes","no")) %>%
mutate(MTx_24=if_else(Peakid %in% MTX_DAR_sig$Peakid,"yes","no")) %>%
mutate(Dox_3=if_else(Peakid %in% DOX_DAR_sig_3$Peakid,"yes","no")) %>%
mutate(Epi_3=if_else(Peakid %in% EPI_DAR_sig_3$Peakid,"yes","no")) %>%
mutate(Dnr_3=if_else(Peakid %in% DNR_DAR_sig_3$Peakid,"yes","no")) %>%
mutate(Mtx_3=if_else(Peakid %in% MTX_DAR_sig_3$Peakid,"yes","no")) %>%
dplyr::select(Peakid,RSID,Dox_24:Mtx_3)
SNP_DAR_overlap_direct Peakid RSID Dox_24 Epi_24 Dnr_24 MTx_24 Dox_3 Epi_3
1 chr18.58336709.58336869 rs12051934 yes yes yes no no no
2 chr2.176086015.176086512 rs6752623 no no no no no no
3 chr22.46249819.46250733 rs7291763 no no yes no no no
4 chr7.134834642.134835353 rs7777356 no yes no no no yes
Dnr_3 Mtx_3
1 no no
2 no no
3 no no
4 yes noDOX_MCF7 added
bind_rows(MCF7_DAR_snp_pairs_dist,peak_snp_pairs_dist) %>%
mutate(sig_24=factor(sig_24, levels= c("sig","not_sig", "MCF7_DAR"))) %>% ggplot(., aes(x= sig_24, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig"),
c("sig","MCF7_DAR"),
c("not_sig","MCF7_DAR")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
ggtitle("DOX 24 hour distances of DAR-SNP pairs\n and non-DAR-SNP pairs with MCF7 DARs")
EPI 24 hours
peak_snp_pairs_dist_EPI <- peak_snp_pairs %>%
mutate(distance = abs(peak_start - snp_start)) %>%
mutate(sig_24= if_else(Peakid %in% EPI_DAR_sig$Peakid, "sig","not_sig"))
peak_snp_pairs_dist_EPI %>%
mutate(sig_24=factor(sig_24, levels= c("sig","not_sig"))) %>%
ggplot(., aes(x= sig_24, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig")),
map_signif_level = FALSE, test = "wilcox.test")
| Version | Author | Date |
|---|---|---|
| 1a9df02 | reneeisnowhere | 2025-07-09 |
wilcox.test(distance ~ sig_24, data = peak_snp_pairs_dist)
Wilcoxon rank sum test with continuity correction
data: distance by sig_24
W = 9463083, p-value = 0.002185
alternative hypothesis: true location shift is not equal to 0Cardiotox_gwas_EPI <- peak_snp_pairs_dist_EPI %>%
dplyr::filter(sig_24=="sig") %>%
group_by(TAD_id,RSID) %>%
slice_min(order_by = distance, with_ties = FALSE) %>%
ungroup() %>%
arrange(snp_chr,snp_start) %>%
left_join(., all_results_pivot, by=c("Peakid"="genes")) %>%
tidyr::unite(., name,Peakid,RSID)EPI_MCF7 added
bind_rows(MCF7_DAR_snp_pairs_dist,peak_snp_pairs_dist_EPI) %>%
mutate(sig_24=factor(sig_24, levels= c("sig","not_sig", "MCF7_DAR"))) %>% ggplot(., aes(x= sig_24, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig"),
c("sig","MCF7_DAR"),
c("not_sig","MCF7_DAR")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
ggtitle("EPI 24 hour distances of DAR-SNP pairs\n and non-DAR-SNP pairs with MCF7 DARs")
peak_snp_pairs_dist_EPI_3 <- peak_snp_pairs %>%
mutate(distance = abs(peak_start - snp_start)) %>%
mutate(sig_3= if_else(Peakid %in% EPI_DAR_sig_3$Peakid, "sig","not_sig"))
peak_snp_pairs_dist_EPI_3 %>%
mutate(sig_3=factor(sig_3, levels= c("sig","not_sig"))) %>%
ggplot(., aes(x= sig_3, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig")),
map_signif_level = FALSE, test = "wilcox.test")
| Version | Author | Date |
|---|---|---|
| 1429820 | reneeisnowhere | 2025-07-21 |
wilcox.test(distance ~ sig_3, data = peak_snp_pairs_dist_EPI_3)
Wilcoxon rank sum test with continuity correction
data: distance by sig_3
W = 3249493, p-value = 3.114e-05
alternative hypothesis: true location shift is not equal to 0Cardiotox_gwas_EPI_3 <- peak_snp_pairs_dist_EPI_3 %>%
dplyr::filter(sig_3=="sig") %>%
group_by(TAD_id,RSID) %>%
slice_min(order_by = distance, with_ties = FALSE) %>%
ungroup() %>%
arrange(snp_chr,snp_start) %>%
left_join(., all_results_pivot, by=c("Peakid"="genes")) %>%
tidyr::unite(., name,Peakid,RSID)DNR 24 hours
peak_snp_pairs_dist_DNR <- peak_snp_pairs %>%
mutate(distance = abs(peak_start - snp_start)) %>%
mutate(sig_24= if_else(Peakid %in% DNR_DAR_sig$Peakid, "sig","not_sig"))
peak_snp_pairs_dist_DNR %>%
mutate(sig_24=factor(sig_24, levels= c("sig","not_sig"))) %>%
ggplot(., aes(x= sig_24, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig")),
map_signif_level = FALSE, test = "wilcox.test")+
ggtitle("DNR 24 hour distances of DAR-SNP pairs and non-DAR-SNP pairs")
| Version | Author | Date |
|---|---|---|
| 1a9df02 | reneeisnowhere | 2025-07-09 |
wilcox.test(distance ~ sig_24, data = peak_snp_pairs_dist)
Wilcoxon rank sum test with continuity correction
data: distance by sig_24
W = 9463083, p-value = 0.002185
alternative hypothesis: true location shift is not equal to 0Cardiotox_gwas_DNR <- peak_snp_pairs_dist_DNR %>%
dplyr::filter(sig_24=="sig") %>%
group_by(TAD_id,RSID) %>%
slice_min(order_by = distance, with_ties = FALSE) %>%
ungroup() %>%
arrange(snp_chr,snp_start) %>%
left_join(., all_results_pivot, by=c("Peakid"="genes")) %>%
tidyr::unite(., name,Peakid,RSID)DNR_MCF7 added
bind_rows(MCF7_DAR_snp_pairs_dist,peak_snp_pairs_dist_DNR) %>%
mutate(sig_24=factor(sig_24, levels= c("sig","not_sig", "MCF7_DAR"))) %>% ggplot(., aes(x= sig_24, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig"),
c("sig","MCF7_DAR"),
c("not_sig","MCF7_DAR")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
ggtitle("DNR 24 hour distances of DAR-SNP pairs\n and non-DAR-SNP pairs with MCF7 DARs")
peak_snp_pairs_dist_DNR_3 <- peak_snp_pairs %>%
mutate(distance = abs(peak_start - snp_start)) %>%
mutate(sig_3= if_else(Peakid %in% DNR_DAR_sig_3$Peakid, "sig","not_sig"))
peak_snp_pairs_dist_DNR_3 %>%
mutate(sig_3=factor(sig_3, levels= c("sig","not_sig"))) %>%
ggplot(., aes(x= sig_3, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig")),
map_signif_level = FALSE, test = "wilcox.test")+
ggtitle("3 hour DNR")
| Version | Author | Date |
|---|---|---|
| 1429820 | reneeisnowhere | 2025-07-21 |
wilcox.test(distance ~ sig_3, data = peak_snp_pairs_dist_DNR_3)
Wilcoxon rank sum test with continuity correction
data: distance by sig_3
W = 4576878, p-value = 0.02023
alternative hypothesis: true location shift is not equal to 0Cardiotox_gwas_DNR_3 <- peak_snp_pairs_dist_DNR_3 %>%
dplyr::filter(sig_3=="sig") %>%
group_by(TAD_id,RSID) %>%
slice_min(order_by = distance, with_ties = FALSE) %>%
ungroup() %>%
arrange(snp_chr,snp_start) %>%
left_join(., all_results_pivot, by=c("Peakid"="genes")) %>%
tidyr::unite(., name,Peakid,RSID)MTX 24 hours
peak_snp_pairs_dist_MTX <- peak_snp_pairs %>%
mutate(distance = abs(peak_start - snp_start)) %>%
mutate(sig_24= if_else(Peakid %in% MTX_DAR_sig$Peakid, "sig","not_sig"))
peak_snp_pairs_dist_MTX %>%
mutate(sig_24=factor(sig_24, levels= c("sig","not_sig"))) %>%
ggplot(., aes(x= sig_24, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig")),
map_signif_level = FALSE, test = "wilcox.test")+
ggtitle("MTX 24 hour distances of DAR-SNP pairs and non-DAR-SNP pairs")
| Version | Author | Date |
|---|---|---|
| 1a9df02 | reneeisnowhere | 2025-07-09 |
wilcox.test(distance ~ sig_24, data = peak_snp_pairs_dist)
Wilcoxon rank sum test with continuity correction
data: distance by sig_24
W = 9463083, p-value = 0.002185
alternative hypothesis: true location shift is not equal to 0Cardiotox_gwas_MTX <- peak_snp_pairs_dist_MTX %>%
dplyr::filter(sig_24=="sig") %>%
group_by(TAD_id,RSID) %>%
slice_min(order_by = distance, with_ties = FALSE) %>%
ungroup() %>%
arrange(snp_chr,snp_start) %>%
left_join(., all_results_pivot, by=c("Peakid"="genes")) %>%
tidyr::unite(., name,Peakid,RSID)MTX_MCF7 added
bind_rows(MCF7_DAR_snp_pairs_dist,peak_snp_pairs_dist_MTX) %>%
mutate(sig_24=factor(sig_24, levels= c("sig","not_sig", "MCF7_DAR"))) %>% ggplot(., aes(x= sig_24, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig"),
c("sig","MCF7_DAR"),
c("not_sig","MCF7_DAR")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
ggtitle("MTX 24 hour distances of DAR-SNP pairs\n and non-DAR-SNP pairs with MCF7 DARs")
peak_snp_pairs_dist_MTX_3 <- peak_snp_pairs %>%
mutate(distance = abs(peak_start - snp_start)) %>%
mutate(sig_3= if_else(Peakid %in% MTX_DAR_sig_3$Peakid, "sig","not_sig"))
peak_snp_pairs_dist_MTX_3 %>%
mutate(sig_3=factor(sig_3, levels= c("sig","not_sig"))) %>%
ggplot(., aes(x= sig_3, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig")),
map_signif_level = FALSE, test = "wilcox.test")+
ggtitle("3 hour MTX")
| Version | Author | Date |
|---|---|---|
| 1429820 | reneeisnowhere | 2025-07-21 |
wilcox.test(distance ~ sig_3, data = peak_snp_pairs_dist_MTX_3)
Wilcoxon rank sum test with continuity correction
data: distance by sig_3
W = 219052, p-value = 0.5511
alternative hypothesis: true location shift is not equal to 0Cardiotox_gwas_MTX_3 <- peak_snp_pairs_dist_MTX_3 %>%
dplyr::filter(sig_3=="sig") %>%
group_by(TAD_id,RSID) %>%
slice_min(order_by = distance, with_ties = FALSE) %>%
ungroup() %>%
arrange(snp_chr,snp_start) %>%
left_join(., all_results_pivot, by=c("Peakid"="genes")) %>%
tidyr::unite(., name,Peakid,RSID)Creating SNP_TAD distance DF
For combining the above 24 hour trt-distance to SNP data frames for box-plots
drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
SNP_TAD_dist_DF <- bind_rows((peak_snp_pairs_dist_MTX %>%
mutate(trt="MTX")),
(peak_snp_pairs_dist %>%
mutate(trt="DOX"))) %>%
bind_rows(.,(peak_snp_pairs_dist_EPI %>%
mutate(trt="EPI"))) %>%
bind_rows(.,(peak_snp_pairs_dist_DNR %>%
mutate(trt="DNR"))) %>%
mutate(trt=factor(trt,levels=c("DOX","EPI","DNR","MTX"))) %>%
mutate(sig_24=factor(sig_24, levels= c("sig","not_sig")))
SNP_TAD_dist_DF%>%
ggplot(., aes(x= interaction(sig_24,trt), y=distance))+
geom_boxplot(aes(fill=trt))+
theme_bw()+
geom_signif(comparisons = list(c("sig.DOX", "not_sig.DOX"),
c("sig.EPI","not_sig.EPI"),
c("sig.DNR", "not_sig.DNR"),
c("sig.MTX", "not_sig.MTX")),
# step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
ggtitle("ALL dist 24 hours")+
scale_fill_manual(values=drug_pal)
SNP_TAD_dist_DF_3 <- bind_rows((peak_snp_pairs_dist_MTX_3 %>%
mutate(trt="MTX")),
(peak_snp_pairs_dist_DOX_3 %>%
mutate(trt="DOX"))) %>%
bind_rows(.,(peak_snp_pairs_dist_EPI_3 %>%
mutate(trt="EPI"))) %>%
bind_rows(.,(peak_snp_pairs_dist_DNR_3 %>%
mutate(trt="DNR"))) %>%
mutate(trt=factor(trt,levels=c("DOX","EPI","DNR","MTX"))) %>%
mutate(sig_3=factor(sig_3, levels= c("sig","not_sig")))
SNP_TAD_dist_DF_3%>%
ggplot(., aes(x= interaction(sig_3,trt), y=distance))+
geom_boxplot(aes(fill=trt))+
theme_bw()+
geom_signif(comparisons = list(c("sig.DOX", "not_sig.DOX"),
c("sig.EPI","not_sig.EPI"),
c("sig.DNR", "not_sig.DNR"),
c("sig.MTX", "not_sig.MTX")),
# step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
ggtitle("ALL dist 3 hours")+
scale_fill_manual(values=drug_pal)
MCF7 tissue specific, non-tissue specific.
Here I am overlapping my data with the MCF7 DAR data. This will create DARS for each treatment that overlap MCF7 DARS (tissue-shared regions) and DARS that do not overlap MCF7 DARS (tissue-specific regions). I will then calculate the distance between the SNPs in the shared vs specific for each treatment.
MCF7_DARs_hyper <- read_excel("C:/Users/renee/Downloads/MCF7-doxATAC/Table 4.XLSX",
sheet = "hyper") %>% GRanges()
MCF7_DARs_hypo <- read_excel("C:/Users/renee/Downloads/MCF7-doxATAC/Table 4.XLSX",
sheet = "hypo") %>% GRanges()
# MCF7_ARsmcf7_1 <- read_excel("C:/Users/renee/Downloads/MCF7-doxATAC/Table 3.XLSX") %>%
# GRanges()
MCF7_DARs_hyper$names <- paste0("hyper_", seq_along(seqnames(MCF7_DARs_hyper)))
MCF7_DARs_hypo$names <- paste0("hypo_", seq_along(seqnames(MCF7_DARs_hypo)))
MCF7_DAR_all <- c(MCF7_DARs_hyper,MCF7_DARs_hypo)
ch = import.chain("C:/Users/renee/ATAC_folder/liftOver_genome/hg19ToHg38.over.chain")
# MCF7_ARsmcf7_1_LO <- as.data.frame(liftOver(MCF7_ARsmcf7_1,ch)) %>%
# GRanges()
MCF7_DARs_hyper_LO <- as.data.frame(liftOver(MCF7_DARs_hyper,ch)) %>%
GRanges()
MCF7_DARs_hypo_LO <- as.data.frame(liftOver(MCF7_DARs_hypo,ch)) %>%
GRanges()
MCF7_DAR_all_LO <- c(MCF7_DARs_hyper_LO,MCF7_DARs_hypo_LO)
MCF7DAR_AR_ol <- join_overlap_intersect(all_regions_gr, MCF7_DAR_all_LO)
DAR_AR_overlap_df <-MCF7DAR_AR_ol %>%
as.data.frame() %>%
distinct(Peakid)Attempting with DOX
peak_snp_pairs_dist %>%
dplyr::filter(sig_24 =="sig") %>%
mutate(specific=if_else(Peakid %in%DAR_AR_overlap_df$Peakid,"specific","not_specific")) %>%
mutate(specific=factor(specific,levels= c("specific","not_specific"))) %>%
ggplot(.,aes(x=specific, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("specific", "not_specific")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
ggtitle("DOX 24 hour distances of DAR-SNP pairs\n that are specific for iPSC-CMs or not-specific")
| Version | Author | Date |
|---|---|---|
| 1429820 | reneeisnowhere | 2025-07-21 |
peak_snp_pairs_dist_EPI %>%
dplyr::filter(sig_24 =="sig") %>%
mutate(specific=if_else(Peakid %in%DAR_AR_overlap_df$Peakid,"specific","not_specific")) %>%
mutate(specific=factor(specific,levels= c("specific","not_specific"))) %>%
ggplot(.,aes(x=specific, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("specific", "not_specific")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
ggtitle("EPI 24 hour distances of DAR-SNP pairs\n that are specific for iPSC-CMs or not-specific")
| Version | Author | Date |
|---|---|---|
| 1429820 | reneeisnowhere | 2025-07-21 |
peak_snp_pairs_dist_DNR %>%
dplyr::filter(sig_24 =="sig") %>%
mutate(specific=if_else(Peakid %in%DAR_AR_overlap_df$Peakid,"specific","not_specific")) %>%
mutate(specific=factor(specific,levels= c("specific","not_specific"))) %>%
ggplot(.,aes(x=specific, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("specific", "not_specific")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
ggtitle("DNR 24 hour distances of DAR-SNP pairs\n that are specific for iPSC-CMs or not-specific")
| Version | Author | Date |
|---|---|---|
| 1429820 | reneeisnowhere | 2025-07-21 |
peak_snp_pairs_dist_MTX %>%
dplyr::filter(sig_24 =="sig") %>%
mutate(specific=if_else(Peakid %in%DAR_AR_overlap_df$Peakid,"specific","not_specific")) %>%
mutate(specific=factor(specific,levels= c("specific","not_specific"))) %>%
ggplot(.,aes(x=specific, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("specific", "not_specific")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
ggtitle("MTX 24 hour distances of DAR-SNP pairs\n that are specific for iPSC-CMs or not-specific")
| Version | Author | Date |
|---|---|---|
| 1429820 | reneeisnowhere | 2025-07-21 |
peak_snp_pairs_dist %>%
dplyr::filter(!Peakid %in% DAR_AR_overlap_df$Peakid) %>%
mutate(sig_24=factor(sig_24,levels = c ("sig","not_sig"))) %>%
ggplot(.,aes(x=sig_24, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
ggtitle("DOX 24 hour distances of DAR-SNP pairs\n that are specific for iPSC-CMs")
| Version | Author | Date |
|---|---|---|
| 1429820 | reneeisnowhere | 2025-07-21 |
peak_snp_pairs_dist_EPI %>%
dplyr::filter(!Peakid %in% DAR_AR_overlap_df$Peakid) %>%
mutate(sig_24=factor(sig_24,levels = c ("sig","not_sig"))) %>%
ggplot(.,aes(x=sig_24, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
ggtitle("EPI 24 hour distances of DAR-SNP pairs\n that are specific for iPSC-CMs")
| Version | Author | Date |
|---|---|---|
| 1429820 | reneeisnowhere | 2025-07-21 |
peak_snp_pairs_dist_DNR %>%
dplyr::filter(!Peakid %in% DAR_AR_overlap_df$Peakid) %>%
mutate(sig_24=factor(sig_24,levels = c ("sig","not_sig"))) %>%
ggplot(.,aes(x=sig_24, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
ggtitle("DNR 24 hour distances of DAR-SNP pairs\n that are specific for iPSC-CMs")
| Version | Author | Date |
|---|---|---|
| 1429820 | reneeisnowhere | 2025-07-21 |
peak_snp_pairs_dist_MTX %>%
dplyr::filter(!Peakid %in% DAR_AR_overlap_df$Peakid) %>%
mutate(sig_24=factor(sig_24,levels = c ("sig","not_sig"))) %>%
ggplot(.,aes(x=sig_24, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
ggtitle("MTX 24 hour distances of DAR-SNP pairs\n that are specific for iPSC-CMs")
| Version | Author | Date |
|---|---|---|
| 1429820 | reneeisnowhere | 2025-07-21 |
Attempt at heatmap with RNA expression, decided not to use or plot here.
Cardotox_mat <- Cardiotox_gwas_collaped_df %>%
dplyr::select(name,DOX_3:TRZ_24,`3_RNA_DOX`,`3_RNA_EPI`,`3_RNA_DNR`,`3_RNA_MTX`,`3_RNA_TRZ`,`24_RNA_DOX`,`24_RNA_EPI`,`24_RNA_DNR`,`24_RNA_MTX`,`24_RNA_TRZ`) %>%
column_to_rownames("name") %>%
as.matrix()
annot_map_df <- Cardiotox_gwas_collaped_df %>%
dplyr::select(name,snp_dist) %>%
column_to_rownames("name")
annot_map <-
rowAnnotation(
snp_dist=Cardiotox_gwas_collaped_df$snp_dist,
TAD_id=Cardiotox_gwas_collaped_df$TAD_id,
col= list(snp_dist=c("2kb"="goldenrod4",
"20kb"="pink",
">20kb"="tan2"),
TAD_id=tad_colors))
simply_map_lfc <- ComplexHeatmap::Heatmap(Cardotox_mat,
# col = col_fun,
left_annotation = annot_map,
show_row_names = TRUE,
row_names_max_width= ComplexHeatmap::max_text_width(rownames(Cardotox_mat), gp=gpar(fontsize=14)),
heatmap_legend_param = list(direction = "horizontal"),
show_column_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE)
ComplexHeatmap::draw(simply_map_lfc,
merge_legend = TRUE,
heatmap_legend_side = "left",
annotation_legend_side = "left")Alternative of final graph
ATAC_all_adj.pvals <- all_results%>%
dplyr::select(source,genes,adj.P.Val) %>%
pivot_wider(id_cols=genes, values_from = adj.P.Val, names_from = source)
# saveRDS(ATAC_all_adj.pvals,"data/Final_four_data/re_analysis/ATAC_all_adj_pvals.RDS")
sig_mat_cardiotox <- ATAC_all_adj.pvals %>%
dplyr::filter(genes %in% peak_snp_pairs_dist$Peakid) %>%
left_join(peak_snp_pairs_dist, by=c("genes"="Peakid")) %>%
group_by(TAD_id,RSID) %>%
slice_min(order_by = distance, with_ties = FALSE) %>%
ungroup() %>%
arrange(snp_chr,snp_start) %>%
group_by(genes, peak_chr, peak_start, TAD_id, sig_24) %>%
summarise(
min_distance = min(distance),
mean_distance = mean(distance),
snp_list = paste(unique(RSID), collapse = ","),
.groups = "drop"
) %>%
left_join(ATAC_all_adj.pvals) %>%
tidyr::unite(., name,genes,snp_list) %>%
dplyr::select(name, DNR_3:TRZ_24) %>%
column_to_rownames("name") %>%
as.matrix()
AR_Cardiotox_gwas_collaped_df <-
peak_snp_pairs_dist %>%
# dplyr::filter(sig_24=="sig") %>%
group_by(TAD_id,RSID) %>%
slice_min(order_by = distance, with_ties = FALSE) %>%
ungroup() %>%
arrange(snp_chr,snp_start) %>%
group_by(Peakid, peak_chr, peak_start, TAD_id, sig_24) %>%
summarise(
min_distance = min(distance),
mean_distance = mean(distance),
snp_list = paste(unique(RSID), collapse = ","),
.groups = "drop"
) %>%
left_join(., all_results_pivot, by=c("Peakid"="genes")) %>%
tidyr::unite(., name,Peakid,snp_list) %>%
mutate(snp_dist=case_when(min_distance <2000 ~"2kb",
min_distance > 2000 & min_distance<20000 ~ "20kb",
min_distance >20000 ~">20kb"))
Cardotox_mat_2 <- AR_Cardiotox_gwas_collaped_df %>%
dplyr::select(name,DOX_3:TRZ_24) %>%
column_to_rownames("name") %>%
as.matrix()
annot_map_df_2 <- AR_Cardiotox_gwas_collaped_df %>%
dplyr::select(name,snp_dist,sig_24) %>%
column_to_rownames("name")
annot_map_2 <-
ComplexHeatmap::rowAnnotation(
snp_dist=AR_Cardiotox_gwas_collaped_df$snp_dist,
TAD_id=AR_Cardiotox_gwas_collaped_df$TAD_id,
DOX_24hr_DAR=AR_Cardiotox_gwas_collaped_df$sig_24,
col= list(snp_dist=c("2kb"="goldenrod4",
"20kb"="pink",
">20kb"="tan2"),
TAD_id=tad_colors))
# all.equal(rownames(sig_mat_cardiotox), rownames(Cardotox_mat_2))
# all.equal(colnames(sig_mat_cardiotox), colnames(Cardotox_mat_2))
#
# setdiff(colnames(sig_mat_cardiotox), colnames(Cardotox_mat_2))
# setdiff(colnames(Cardotox_mat_2), colnames(sig_mat_cardiotox))
#
# intersect(colnames(sig_mat_cardiotox), colnames(Cardotox_mat_2))
# setdiff(colnames(sig_mat_cardiotox), colnames(Cardotox_mat_2))
# setdiff(colnames(Cardotox_mat_2), colnames(sig_mat_cardiotox))
simply_map_lfc_2 <- ComplexHeatmap::Heatmap(Cardotox_mat_2,
left_annotation = annot_map_2,
show_row_names = TRUE,
row_names_max_width= ComplexHeatmap::max_text_width(rownames(Cardotox_mat_2), gp=gpar(fontsize=14)),
heatmap_legend_param = list(direction = "horizontal"),
show_column_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE,
cell_fun = function(j, i, x, y, width, height, fill) {
rowname <- rownames(Cardotox_mat_2)[i]
colname <- colnames(Cardotox_mat_2)[j]
if (!is.na(sig_mat_cardiotox[rowname, colname]) &&
sig_mat_cardiotox[rowname, colname] < 0.05) {
grid.text("*", x, y, gp = gpar(fontsize = 20))
}
})
ComplexHeatmap::draw(simply_map_lfc_2,
merge_legend = TRUE,
heatmap_legend_side = "left",
annotation_legend_side = "left")
| Version | Author | Date |
|---|---|---|
| b1638d9 | reneeisnowhere | 2025-07-29 |
adding in Park data:
ParkSNPs <- readRDS("data/other_papers/ParkSNPs_pull_VEF.RDS")
ParkSNP_table <-
ParkSNPs %>%
dplyr::select(1:2) %>%
distinct() %>%
separate_wider_delim(.,Location,delim=":",names=c("chr","position"), cols_remove=FALSE) %>%
separate_wider_delim(.,position,delim="-",names=c("begin","term")) %>%
mutate(chr=paste0("chr",chr))
ParkSNP_gr <- ParkSNP_table %>%
mutate("start" = begin, "end"=term) %>%
GRanges()
Park_snp_tad_df <- join_overlap_inner(ParkSNP_gr, Left_ventricle_TAD) %>%
as_tibble() %>%
dplyr::rename("RSID"=X.Uploaded_variation) %>%
dplyr::select(RSID, snp_start = start, snp_chr = seqnames, TAD_id)
Park_snp_pairs <- peak_tad_df %>%
inner_join(Park_snp_tad_df, by = "TAD_id")
Park_snp_pairs %>%
distinct(RSID)
Park_snp_pairs_dist <- Park_snp_pairs %>%
mutate(distance = abs(peak_start - snp_start)) %>%
mutate(sig_24= if_else(Peakid %in% DOX_DAR_sig$Peakid, "sig","not_sig"))
Park_snp_pairs_dist %>%
mutate(sig_24=factor(sig_24, levels= c("sig","not_sig"))) %>%
ggplot(., aes(x= sig_24, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig")),
map_signif_level = FALSE, test = "wilcox.test")
wilcox.test(distance ~ sig_24, data = Park_snp_pairs_dist)
Park_Cardiotox_gwas_df <- Park_snp_pairs_dist %>%
dplyr::filter(sig_24=="sig") %>%
group_by(TAD_id,RSID) %>%
slice_min(order_by = distance, with_ties = FALSE) %>%
ungroup() %>%
arrange(snp_chr,snp_start) %>%
left_join(., all_results_pivot, by=c("Peakid"="genes")) %>%
tidyr::unite(., name,Peakid,RSID)Park_Cardiotox_gwas_collaped_df <-
Park_snp_pairs_dist %>%
# dplyr::filter(sig_24=="sig") %>%
group_by(TAD_id,RSID) %>%
slice_min(order_by = distance, with_ties = FALSE) %>%
ungroup() %>%
arrange(snp_chr,snp_start) %>%
group_by(Peakid, peak_chr, peak_start, TAD_id, sig_24) %>%
summarise(
min_distance = min(distance),
mean_distance = mean(distance),
snp_list = paste(unique(RSID), collapse = ","),
.groups = "drop"
) %>%
left_join(., all_results_pivot, by=c("Peakid"="genes")) %>%
# left_join(., Peak_gene_RNA_LFC, by=c("Peakid"="Peakid")) %>%
# left_join(.,gene_info_collapsed, by=c("geneId"="ENTREZID")) %>%
# mutate(SYMBOL=if_else(is.na(SYMBOL.x),SYMBOL.y,if_else(SYMBOL.x==SYMBOL.y, SYMBOL.x,paste0(SYMBOL.x,"_",SYMBOL.y)))) %>%
tidyr::unite(., name,Peakid,snp_list) %>%
mutate(snp_dist=case_when(min_distance <2000 ~"2kb",
min_distance > 2000 & min_distance<20000 ~ "20kb",
min_distance >20000 ~">20kb"))
Cardotox_mat_park <- Park_Cardiotox_gwas_collaped_df %>%
dplyr::select(name,DOX_3:TRZ_24) %>%
column_to_rownames("name") %>%
as.matrix()
annot_map_df_park <- Park_Cardiotox_gwas_collaped_df %>%
dplyr::select(name,snp_dist,sig_24) %>%
column_to_rownames("name")
annot_map_park <-
ComplexHeatmap::rowAnnotation(
snp_dist=Park_Cardiotox_gwas_collaped_df$snp_dist,
TAD_id=Park_Cardiotox_gwas_collaped_df$TAD_id,
DOX_24hr_DAR=Park_Cardiotox_gwas_collaped_df$sig_24,
col= list(snp_dist=c("2kb"="goldenrod4",
"20kb"="pink",
">20kb"="tan2")))
simply_map_lfc_park <- ComplexHeatmap::Heatmap(Cardotox_mat_park,
# col = col_fun,
left_annotation = annot_map_park,
show_row_names = TRUE,
row_names_max_width= ComplexHeatmap::max_text_width(rownames(Cardotox_mat_park), gp=gpar(fontsize=14)),
heatmap_legend_param = list(direction = "horizontal"),
show_column_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE)
ComplexHeatmap::draw(simply_map_lfc_park,
merge_legend = TRUE,
heatmap_legend_side = "left",
annotation_legend_side = "left")Accessibility changes of SNP- directly overlapping DARs
drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
raw_counts <- read_delim("data/Final_four_data/re_analysis/Raw_unfiltered_counts.tsv",delim="\t") %>%
column_to_rownames("Peakid") %>%
as.matrix()
lcpm <- cpm(raw_counts, log= TRUE)
### for determining the basic cutoffs
filt_raw_counts <- raw_counts[rowMeans(lcpm)> 0,]
filt_raw_counts_noY <- filt_raw_counts[!grepl("chrY",rownames(filt_raw_counts)),]
ATAC_adj.pvals <-all_results %>%
dplyr::select(source,genes,adj.P.Val) %>%
dplyr::filter(genes %in% SNP_DAR_overlap_direct$Peakid) %>%
separate(source, into = c("trt", "time")) %>%
mutate(
time = paste0(time, "h"), # convert "3" → "3h"
trt = factor(trt, levels = c("DOX", "EPI", "DNR", "MTX", "TRZ")),
group=paste0(trt,"_",time)) %>%
mutate(group=factor(group,levels = c("DOX_3h", "EPI_3h", "DNR_3h", "MTX_3h", "TRZ_3h", "VEH_3h",
"DOX_24h", "EPI_24h", "DNR_24h", "MTX_24h", "TRZ_24h", "VEH_24h"))) %>%
dplyr::rename("Peakid"=genes)ATAC_counts_lcpm <- filt_raw_counts_noY %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
rownames_to_column("Peakid")for (peak in SNP_DAR_overlap_direct$Peakid) {
PEAK <- SNP_DAR_overlap_direct$Peakid[SNP_DAR_overlap_direct$Peakid == peak]
# Prep expression data
peak_expr <- ATAC_counts_lcpm %>%
filter(Peakid == peak) %>%
pivot_longer(cols = !Peakid, names_to = "sample", values_to = "lcpm") %>%
separate(sample, into = c("ind", "trt", "time")) %>%
mutate(
time = paste0(time), # if already "3h"/"24h"
group = paste0(trt, "_", time),
group = factor(group, levels = c(
"DOX_3h", "EPI_3h", "DNR_3h", "MTX_3h", "TRZ_3h", "VEH_3h",
"DOX_24h", "EPI_24h", "DNR_24h", "MTX_24h", "TRZ_24h", "VEH_24h"
))
)
# Get peak-specific p-values
peak_pvals <- ATAC_adj.pvals %>%
filter(Peakid==peak)
# Merge in p-values by group
peak_plot_data <- left_join(peak_expr, peak_pvals, by = c("Peakid", "group", "time"))
# Create label position below box
label_positions <- peak_plot_data %>%
group_by(group) %>%
summarise(y = min(lcpm, na.rm = TRUE) - 0.5, .groups = "drop")
peak_plot_data <- left_join(peak_plot_data, label_positions, by = "group")
peak_plot_data <- peak_plot_data %>%
separate(group, into = c("trt", "time"), sep = "_", remove = FALSE)
# Plot
peak_plot <- ggplot(peak_plot_data, aes(x = group, y = lcpm)) +
geom_boxplot(aes(fill = trt)) +
geom_text(
aes(y = y,
label = ifelse(trt != "VEH" & !is.na(adj.P.Val),
paste0("", signif(adj.P.Val, 2)),
"")),
size = 3,
vjust = 1.2
) +
scale_fill_manual(values = drug_pal) +
theme_bw() +
ggtitle(paste0("ATAC Log2cpm of ", PEAK)) +
ylab("log2 cpm ATAC") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
plot(peak_plot)
}
# for (peak in SNP_DAR_overlap_direct$Peakid) {
# PEAK <- SNP_DAR_overlap_direct$Peakid[SNP_DAR_overlap_direct$Peakid == peak]
#
#
# # Filter and plot
# gene_plot <- ATAC_counts_lcpm %>%
# filter(Peakid == peak) %>%
# pivot_longer(cols = !Peakid, names_to = "sample", values_to = "lcpm") %>%
# separate(sample, into = c("trt", "ind", "time")) %>%
# mutate(
# time = factor(time, levels = c("3h", "24h")),
# trt = factor(trt, levels = c("DOX", "EPI", "DNR", "MTX", "TRZ", "VEH"))
# ) %>%
# ggplot(aes(x = time, y = counts)) +
# geom_boxplot(aes(fill = trt)) +
# scale_fill_manual(values = drug_pal) +
# theme_bw() +
# ylab("log2 cpm RNA") +
# ggtitle(paste0(" Log2cpm of ", PEAK))
#
# plot(gene_plot)
# }
#
filt_raw_counts_noY %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
rownames_to_column("Peakid") %>%
dplyr::filter(Peakid %in% SNP_DAR_overlap_direct$Peakid) %>%
pivot_longer(., cols= !Peakid, names_to = "sample",values_to = "log2cpm") %>%
separate_wider_delim(, cols=sample, names =c("ind","trt","time"),delim="_",cols_remove = FALSE) %>%
mutate(
time = factor(time, levels = c("3h", "24h")),
trt = factor(trt, levels = c("DOX", "EPI", "DNR", "MTX", "TRZ", "VEH"))
) %>%
ggplot(aes(x = time, y = log2cpm)) +
geom_boxplot(aes(fill = trt)) +
scale_fill_manual(values = drug_pal) +
theme_bw() +
facet_wrap(~Peakid, scales="free_y")+
ylab("log2 cpm ATAC regions") 
DOX_dar_diff <- DOX_DAR_24hr_table %>%
dplyr::filter(mcols.adj.P.Val<0.05)%>%
dplyr::rename("Peakid"=mcols.genes)# TAD_SNP_Peak_ol %>%
# dplyr::filter(TAD_id =="TAD_102") %>%
# dplyr::filter( Peakid%in%DOX_DAR_sig$Peakid)
filt_raw_counts_noY %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
rownames_to_column("Peakid") %>%
dplyr::filter(Peakid =="chr1.173823770.173825267") %>%
pivot_longer(., cols= !Peakid, names_to = "sample",values_to = "log2cpm") %>%
separate_wider_delim(, cols=sample, names =c("ind","trt","time"),delim="_",cols_remove = FALSE) %>%
mutate(
time = factor(time, levels = c("3h", "24h")),
trt = factor(trt, levels = c("DOX", "EPI", "DNR", "MTX", "TRZ", "VEH"))
) %>%
ggplot(aes(x = time, y = log2cpm)) +
geom_boxplot(aes(fill = trt)) +
scale_fill_manual(values = drug_pal) +
theme_bw() +
facet_wrap(~Peakid, scales="free_y")+
ylab("log2 cpm ATAC regions") 
SNP DAR direct overlap heatmap
SNP_DAR_overlap_mat <-
SNP_DAR_overlap_direct %>%
dplyr::select(Peakid,RSID) %>%
left_join(., snp_tad_df,by= c("RSID"="RSID")) %>%
dplyr::select(Peakid, TAD_id, RSID) %>%
left_join(., all_results_pivot, by=c("Peakid"="genes")) %>%
tidyr::unite(., name,Peakid,RSID)
SNP_DAR_sig_mat <- SNP_DAR_overlap_direct %>%
dplyr::select(Peakid,RSID) %>%
left_join(., snp_tad_df,by= c("RSID"="RSID")) %>%
dplyr::select(Peakid, TAD_id, RSID) %>%
left_join(., ATAC_all_adj.pvals, by=c("Peakid"="genes")) %>%
tidyr::unite(., name,Peakid,RSID) %>%
column_to_rownames("name") %>%
as.matrix()
Cardotox_mat_3 <- SNP_DAR_overlap_mat %>%
dplyr::select(name,DOX_3:TRZ_24) %>%
column_to_rownames("name") %>%
as.matrix()
annot_map_df_3 <- SNP_DAR_overlap_mat %>%
dplyr::select(name,TAD_id) %>%
column_to_rownames("name")
annot_map_3 <-
ComplexHeatmap::rowAnnotation(TAD_id=SNP_DAR_overlap_mat$TAD_id)
simply_map_lfc_3 <- ComplexHeatmap::Heatmap(Cardotox_mat_3,
# col = col_fun,
left_annotation = annot_map_3,
column_title="Cardiotox SNP direct overlaps",
show_row_names = TRUE,
row_names_max_width= ComplexHeatmap::max_text_width(rownames(Cardotox_mat_3), gp=gpar(fontsize=14)),
heatmap_legend_param = list(direction = "horizontal"),
show_column_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE,
cell_fun = function(j, i, x, y, width, height, fill) {
rowname <- rownames(Cardotox_mat_3)[i]
colname <- colnames(Cardotox_mat_3)[j]
if (!is.na(SNP_DAR_sig_mat[rowname, colname]) &&
SNP_DAR_sig_mat[rowname, colname] < 0.05) {
grid.text("*", x, y, gp = gpar(fontsize = 20))
}
})
ComplexHeatmap::draw(simply_map_lfc_3,
merge_legend = TRUE,
heatmap_legend_side = "left",
annotation_legend_side = "left")
| Version | Author | Date |
|---|---|---|
| b1638d9 | reneeisnowhere | 2025-07-29 |
sessionInfo()R version 4.4.2 (2024-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] BSgenome.Hsapiens.UCSC.hg38_1.4.5
[2] BSgenome_1.74.0
[3] BiocIO_1.16.0
[4] Biostrings_2.74.1
[5] XVector_0.46.0
[6] liftOver_1.30.0
[7] Homo.sapiens_1.3.1
[8] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[9] GO.db_3.20.0
[10] OrganismDbi_1.48.0
[11] gwascat_2.38.0
[12] ComplexHeatmap_2.22.0
[13] readxl_1.4.5
[14] circlize_0.4.16
[15] epitools_0.5-10.1
[16] ggrepel_0.9.6
[17] plyranges_1.26.0
[18] ggsignif_0.6.4
[19] genomation_1.38.0
[20] smplot2_0.2.5
[21] eulerr_7.0.2
[22] biomaRt_2.62.1
[23] devtools_2.4.5
[24] usethis_3.1.0
[25] ggpubr_0.6.1
[26] BiocParallel_1.40.2
[27] scales_1.4.0
[28] VennDiagram_1.7.3
[29] futile.logger_1.4.3
[30] gridExtra_2.3
[31] ggfortify_0.4.18
[32] edgeR_4.4.2
[33] limma_3.62.2
[34] rtracklayer_1.66.0
[35] org.Hs.eg.db_3.20.0
[36] TxDb.Hsapiens.UCSC.hg38.knownGene_3.20.0
[37] GenomicFeatures_1.58.0
[38] AnnotationDbi_1.68.0
[39] Biobase_2.66.0
[40] ChIPpeakAnno_3.40.0
[41] GenomicRanges_1.58.0
[42] GenomeInfoDb_1.42.3
[43] IRanges_2.40.1
[44] S4Vectors_0.44.0
[45] BiocGenerics_0.52.0
[46] ChIPseeker_1.42.1
[47] RColorBrewer_1.1-3
[48] broom_1.0.8
[49] kableExtra_1.4.0
[50] lubridate_1.9.4
[51] forcats_1.0.0
[52] stringr_1.5.1
[53] dplyr_1.1.4
[54] purrr_1.0.4
[55] readr_2.1.5
[56] tidyr_1.3.1
[57] tibble_3.3.0
[58] ggplot2_3.5.2
[59] tidyverse_2.0.0
[60] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] R.methodsS3_1.8.2 dichromat_2.0-0.1
[3] vroom_1.6.5 progress_1.2.3
[5] urlchecker_1.0.1 nnet_7.3-20
[7] vctrs_0.6.5 ggtangle_0.0.7
[9] digest_0.6.37 png_0.1-8
[11] shape_1.4.6.1 git2r_0.36.2
[13] magick_2.8.7 MASS_7.3-65
[15] reshape2_1.4.4 foreach_1.5.2
[17] httpuv_1.6.16 qvalue_2.38.0
[19] withr_3.0.2 xfun_0.52
[21] ggfun_0.1.9 ellipsis_0.3.2
[23] survival_3.8-3 memoise_2.0.1
[25] profvis_0.4.0 systemfonts_1.2.3
[27] tidytree_0.4.6 zoo_1.8-14
[29] GlobalOptions_0.1.2 gtools_3.9.5
[31] R.oo_1.27.1 Formula_1.2-5
[33] prettyunits_1.2.0 KEGGREST_1.46.0
[35] promises_1.3.3 httr_1.4.7
[37] rstatix_0.7.2 restfulr_0.0.16
[39] ps_1.9.1 rstudioapi_0.17.1
[41] UCSC.utils_1.2.0 miniUI_0.1.2
[43] generics_0.1.4 DOSE_4.0.1
[45] base64enc_0.1-3 processx_3.8.6
[47] curl_6.4.0 zlibbioc_1.52.0
[49] GenomeInfoDbData_1.2.13 SparseArray_1.6.2
[51] RBGL_1.82.0 xtable_1.8-4
[53] doParallel_1.0.17 evaluate_1.0.4
[55] S4Arrays_1.6.0 BiocFileCache_2.14.0
[57] hms_1.1.3 colorspace_2.1-1
[59] filelock_1.0.3 magrittr_2.0.3
[61] later_1.4.2 ggtree_3.14.0
[63] lattice_0.22-7 getPass_0.2-4
[65] XML_3.99-0.18 cowplot_1.1.3
[67] matrixStats_1.5.0 Hmisc_5.2-3
[69] pillar_1.11.0 nlme_3.1-168
[71] iterators_1.0.14 pwalign_1.2.0
[73] gridBase_0.4-7 caTools_1.18.3
[75] compiler_4.4.2 stringi_1.8.7
[77] SummarizedExperiment_1.36.0 GenomicAlignments_1.42.0
[79] plyr_1.8.9 crayon_1.5.3
[81] abind_1.4-8 gridGraphics_0.5-1
[83] locfit_1.5-9.12 bit_4.6.0
[85] fastmatch_1.1-6 whisker_0.4.1
[87] codetools_0.2-20 textshaping_1.0.1
[89] bslib_0.9.0 GetoptLong_1.0.5
[91] multtest_2.62.0 mime_0.13
[93] splines_4.4.2 Rcpp_1.1.0
[95] dbplyr_2.5.0 cellranger_1.1.0
[97] utf8_1.2.6 knitr_1.50
[99] blob_1.2.4 clue_0.3-66
[101] AnnotationFilter_1.30.0 fs_1.6.6
[103] checkmate_2.3.2 pkgbuild_1.4.8
[105] ggplotify_0.1.2 Matrix_1.7-3
[107] callr_3.7.6 statmod_1.5.0
[109] tzdb_0.5.0 svglite_2.2.1
[111] pkgconfig_2.0.3 tools_4.4.2
[113] cachem_1.1.0 RSQLite_2.4.1
[115] viridisLite_0.4.2 DBI_1.2.3
[117] impute_1.80.0 fastmap_1.2.0
[119] rmarkdown_2.29 Rsamtools_2.22.0
[121] sass_0.4.10 patchwork_1.3.1
[123] BiocManager_1.30.26 VariantAnnotation_1.52.0
[125] graph_1.84.1 carData_3.0-5
[127] rpart_4.1.24 farver_2.1.2
[129] yaml_2.3.10 MatrixGenerics_1.18.1
[131] foreign_0.8-90 cli_3.6.5
[133] txdbmaker_1.2.1 lifecycle_1.0.4
[135] lambda.r_1.2.4 sessioninfo_1.2.3
[137] backports_1.5.0 timechange_0.3.0
[139] gtable_0.3.6 rjson_0.2.23
[141] parallel_4.4.2 ape_5.8-1
[143] jsonlite_2.0.0 bitops_1.0-9
[145] bit64_4.6.0-1 pwr_1.3-0
[147] yulab.utils_0.2.0 futile.options_1.0.1
[149] jquerylib_0.1.4 GOSemSim_2.32.0
[151] R.utils_2.13.0 snpStats_1.56.0
[153] lazyeval_0.2.2 shiny_1.11.1
[155] htmltools_0.5.8.1 enrichplot_1.26.6
[157] rappdirs_0.3.3 formatR_1.14
[159] ensembldb_2.30.0 glue_1.8.0
[161] httr2_1.1.2 RCurl_1.98-1.17
[163] InteractionSet_1.34.0 rprojroot_2.0.4
[165] treeio_1.30.0 boot_1.3-31
[167] universalmotif_1.24.2 igraph_2.1.4
[169] R6_2.6.1 gplots_3.2.0
[171] labeling_0.4.3 cluster_2.1.8.1
[173] pkgload_1.4.0 regioneR_1.38.0
[175] aplot_0.2.8 DelayedArray_0.32.0
[177] tidyselect_1.2.1 plotrix_3.8-4
[179] ProtGenerics_1.38.0 htmlTable_2.4.3
[181] xml2_1.3.8 car_3.1-3
[183] seqPattern_1.38.0 KernSmooth_2.23-26
[185] data.table_1.17.6 htmlwidgets_1.6.4
[187] fgsea_1.32.4 rlang_1.1.6
[189] remotes_2.5.0 Cairo_1.6-2